Because of the popularity of digital photography, a rapidly increasing number of images in digital form are being created by both professionals and non-professionals. Many software tools are available to assist a photographer in the processing of these digital images. A photographer can use these software tools to manipulate digital images in various ways, such as adjusting the tint, brightness, contrast, size, and so on, to arrive at a high-quality image.
To help evaluate the quality of images, photographers and others would like a software tool that could automatically, accurately, and objectively assess image quality. Such an assessment of image quality could be used for quality control by professional photographers to evaluate image processing systems, to optimize algorithms and parameter settings for image processing, and to help non-professional photographers manage their digital images and assess their expertise.
Prior quality assessment techniques can be categorized as full-reference, reduced-reference, or no-reference techniques. A full-reference technique assesses the quality of a copy of an image based on analysis of differences from the original image. A reduced-reference technique assesses the quality of a copy of an image based on analysis of certain features derived from the original image. A no-reference technique assesses the quality of an image without any reference information. Although human observers can easily assess image quality without reference information, it can be complex and difficult for a software tool to assess image quality without any reference information.
Typical no-reference techniques focus on measuring the distortion within an image. Generally, these no-reference techniques identify a discriminative local feature of each pixel, assess the local distortion of that feature, and average the local distortions over the entire image. These no-reference techniques then use the average distortions to predict image quality that is consistent with a human observer. The local features used by these techniques include blurring, ringing, and blocking.
These local features, however, do not adequately represent the “holistic” image quality assessment performed by human observers. In particular, human observers rely on cognitive and aesthetic information within images, and not solely on distortion, to assess image quality. Research has indicated that scene composition and location as well as the people and their expressions are important attributes for assessing image quality. Because of the difficulty in assessing such subjective aspects of image quality, the no-reference techniques rely on features that can be physically measured such as contrast, sharpness, colorfulness, saturation, and depth of field when assessing image quality. These techniques, however, do not provide an image quality assessment that accurately reflects that of a human observer. It would be desirable to have a no-reference technique that would accurately reflect the subjective image quality of a human observer using objective measurements of an image.